Optical coherence tomography (OCT) is a noninvasive, noncontact imaging modality that uses coherence gating to obtain high-resolution cross-sectional images of tissue microstructure. Several implementations of OCT have been developed. In Frequency domain or Fourier domain OCT (FD-OCT), the interferometric signal between light from a reference and the back-scattered light from a sample point is recorded in the frequency domain either by using a dispersive spectrometer in the detection arm in the case of spectral-domain OCT (SD-OCT) or rapidly tuning a swept laser source in the case of swept-source OCT (SS-OCT). After a wavelength calibration, a one-dimensional Fourier transform is taken to obtain an A-line spatial intensity distribution of the object scattering in the depth dimension.
There are several intensity and/or phase-resolved data based OCT techniques, collectively named OCT Angiography, that have been utilized to map the retinal vasculature or identify regions with flow in the tissue (see for example An et al. “Optical microangiography provides correlation between microstructure and microvasculature of optic nerve head in human subjects,” J. Biomed. Opt. 17, 116018, 2012, Zhao et al., “Doppler standard deviation imaging for clinical monitoring of in vivo human skin blood flow,” Optics Letters 25, 1358-1360, 2000, Fingler et al. “Mobility and transverse flow visualization using phase variance contrast with spectral domain optical coherence tomography” Optics Express. Vol. 15, No. 20. pp 12637-12653, 2007, Makita et al., “Optical Coherence Angiography,” Optics Express, 14(17), 7821-7840, 2006, Mariampillai et al., “Optimized speckle variance OCT imaging of microvasculature,” Optics Letters 35, 1257-1259, 2010, and Wang et al., “Frequency domain phase-resolved optical Doppler and Doppler variance tomography” Optics Communications 242 345-350, 2004 hereby incorporated by reference). OCT Angiography, a non-invasive technique to visualize detailed vasculature or regions of flow, could provide doctors useful clinical information for diagnosis and management of eye diseases.
Conventional techniques to visualize retinal vasculature are invasive in nature, and use pharmacological techniques to modify contrast in the imaged retina. Contemporary clinical practice involves injection of a fluorescent dye (such as fluorescein (FA) or indocyanine green (ICG)) into the systemic circulation, and the eye is then scanned to generate an image, which selectively shows the path of the dye through the vascular network. In typical commercial fundus imaging systems, no information of the depth structure of the vasculature is captured by this method. In contrast, vascular images generated by examining the OCT signal are non-invasive, and provide comparable fidelity in capturing the existing vascular network with blood flow contrast along with its depth encoding. Retinal Function Imager (Optical Imaging Ltd.) used an approach of obtaining fast sequential fundus images by using stroboscopic illumination and generating vasculature maps by tracking motion of erythrocytes or blood cells. While this approach was non-invasive in nature, it lacked the depth information. Confocal laser scanning topography has been used to generate 3D profile of surface of vascular structures in the eye (Schmidt-Erfurth et al., “Three-Dimensional Topographic Angiography in Chorioretinal Vascular Disease,” IOVS, 42 (10), 2386-2394, 2001). However, this approach cannot provide high axial resolution as OCT and is highly susceptible to artifacts caused by eye motion.
Diagnostically, changes to both the vascular and the typically avascular retina are important indicators of developing retinal pathologies. Although visualization of the vascular structure helps in boosting the diagnostic efficacy of this imaging technique, it can be further leveraged by augmenting the visualization with some salient quantifications and metrics derived from the identified vascular and avascular sections of the retina. A primary quantity of interest is the global or structure-specific retinal blood flow kinetics, which can be challenging to quantify because of low flow velocities relative to the temporal resolution of the technique, and the almost perpendicular orientation of the capillaries with respect to the probing beam. In addition to visualization, derived quantifiers from the angiography data which serve to aid in differentiating vascular networks in healthy and diseased eyes are also desirable.
Several research groups have explored quantitative methods for angiography data to construct meaningful numerical indicators of vascular pathology from traditional and OCT angiography data. (see for example Avakian, et al., “Fractal analysis of region-based vascular change in the normal and non-proliferative diabetic retina,” Curr. Eye Res. 24, 274-280, 2002, Schmoll et al. “Imaging of the parafoveal capillary network and its integrity analysis using fractal dimension” Biomed. Opt. Express 2, 1159-1168, 2011, Jia et al., “Quantitative OCT angiography of optic nerve head blood flow,” Biomed. Opt. Express 3, 3127-3137, 2012, An et al., “Optical microangiography provides correlation between microstructure and microvasculature of optic nerve head in human subjects,” J. Biomed. Opt. 17, 116018, 2012 and Jia et al. “OCT Angiography of Optic Disc Perfusion in Glaucoma” Ophthalmology, 121 (7), 1322-1332, 2014).
One piece of important anatomical information that is captured by OCT angiography is the depth information, or the spatial distribution of the vessels in the retinal tissue. To visualize the complex capillary networks and to make use of the additional depth information gained by OCT angiography compared to traditional angiography methods such as FA, OCT angiography data is often displayed as 2D projections with the color encoded depth information (see Kim et al. “In vivo volumetric imaging of human retinal circulation with phase variance OCT,” Biomedical Optics Express, 2(6), 1504-1513 2011). Such 2D projections at least allow distinguishing capillary layers of different depths. They however lack the 3D impression and also do not provide easily accessible information of which larger retinal vessels feed and drain different capillary network regions. Retinal vessel connectivity measures are also known for fundus photography (see for example Al-Diri et al. “Automated analysis of retinal vascular network connectivity,” Computerized Medical Imaging and Graphics, 34, 462-470, 2010 and Ganesan et al. “Development of an Image-Based Network Model of Retinal Vasculature,” Annals of Biomedical Engineering 38(4) 1566-1585, 2010).
Retinal vessels are not always constrained to the layers containing the capillary beds, and there is a need to facilitate the visualization and identification of such vessels. There are several ocular diseases including, but not limited to, macular telangiectasia, choroidal neovascularization (CNV), neovascularization elsewhere (NVE) etc., where there is growth of new vasculature that may abruptly dive through the retinal layers or even cause disruptions in it. In diseases such as Retinal Angiomatous Proliferation (RAP), new vessels grow which connect the retinal vasculature with the choroidal vasculature, with increasing neovascularization at more advanced disease stages. The resulting intra-retinal neovascularization and subretinal neovascularization are important features to identify in the diagnosis and staging of disease. For instance, appropriate clinical treatments and expected outcomes are different for choroidal neovascularization than for so-called masquerades such as RAP. Historically, angiography (FA and ICG) has been used to identify these cases. There is a need for more convenient detection of this neovascularization without the use of invasive imaging dye-based techniques like angiography. Depth resolved imaging by optical coherence tomography can be used for this purpose. For instance, vessels linking retinal and choroidal vasculature can be detected in B-scan intensity images. However, detailed inspection of volumetric data sets as a series of B-scans is impractical in a routine clinical practice. Srivastava et al. recently suggested several ways to improve the display of depth information (US Patent Publication No. 2013/0301008 hereby incorporated by reference). Here we propose further and alternative ways to enhance visualization and improve the clinical value of OCT angiography data.